The present invention relates to a MIMO-OFDM communication system and a MIMO-OFDM communication method thereof, and in particular relates to a MIMO-OFDM communication system and a MIMO-OFDM communication method which enable improvement of system performance and reduction of complexity, in a digital wireless communication system such as a next-generation wireless LAN which send and receive data by means of MIMO-OFDM communication and a spatial multiplexing OFDM system.
Among current wireless communication systems, attention is being paid to spatial multiplexing technology which increases transmission capacity in proportion to the number of transmission antennas, by transmitting different data streams in parallel from a plurality of transmission antennas. Different transmission antennas are placed so as to be mutually uncorrelated, and the data stream transmitted from each of the antennas passes through independent fading transmission path and is received by each of reception antennas.
Here, a plurality of reception antennas, placed so as to be mutually uncorrelated, are utilized in a multiple-input, multiple-output (MIMO) system, thereby a channel correlation matrix with a high degree of freedom can be generated, and the SNR when separating spatial multiplexed data streams can be improved.
FIG. 21 is an explanatory diagram of the configuration of a MIMO system; TR is a transmitting station, and RV is a receiving station. Data streams S1 to SM in the same number as the plurality M of transmission antennas ATT1 to ATTM undergo data modulation, oversampling, D/A conversion, orthogonal modulation, frequency upconversion, band-limiting filtering, and other processing by respective transmission portions TX1 to TXM, and are transmitted from transmission antennas ATT1 to ATTM. Signals transmitted from the antennas ATT1 to ATTM pass through independent fading channels hmn (m=1 to M, n=1 to N), and after spatial multiplexing, are received by N reception antennas ATR1 to ATRN. Signals received by the reception antennas undergo, in respective reception portions RX1 to RXn, filtering, frequency downconversion, orthogonal detection, and A/D conversion processing, and reception data streams y1 to yN are generated. Because the M transmitted data streams are multiplexed into each of the received data streams, by performing signal processing of all of the received data streams in a data processing unit DPU, the transmitted data streams are separated and reproduced.
Algorithms used in signal processing to separate received signals into the transmitted data streams S1 to SM include such linear algorithms as the ZF (Zero-Forcing) algorithm, employing the inverse matrix of the channel correlation matrix, and the MMSE (Minimum Mean Square Error) algorithm, and nonlinear algorithms, of which the BLAST (Bell Laboratories Layered Space-Time) algorithm is representative. In additions, methods which do not use computation of the inverse matrix of the correlation matrix, such as the MLD (Maximum Likelihood Decoding) algorithm, are also known.
If the transmitted data streams are represented by an M-dimensional complex vector S, and the received data streams are represented by an N-dimensional complex vector Y, then the following relation obtains.Y=HS+V  (1)
Here H is an N×M complex channel matrix (h11-hNM) and V is a N-dimensional AWGN vector.
In the ZF algorithm, the following equation is used to estimate a transmitted data stream.Ŝ=(H*H)−1H*Y  (2)
Here “*” indicates the complex conjugate transpose of a matrix, and H*H is called the channel correlation matrix.
In the MMSE algorithm, the following equation is used to estimate transmitted data streams.Ŝ=(H*H+αI)−1H*Yα=σV/σS=M/ρE[SS*]=σSI  (3)
Here ρ is equivalent to the SNR per reception antenna. In MMSE, the need arises to precisely estimate the SNR; but because the effect of noise emphasis can be reduced, in general characteristics are superior to those of the ZF algorithm.
In the MLD algorithm, the following equation is used to estimate transmitted data streams.
                              S          ^                =                                            arg              ⁢                                                          ⁢                                                min                  k                                ⁢                                                                                                                          Y                        -                                                  HS                          k                                                                                                            2                                    ⁢                                                                          ⁢                                      S                    k                                                                        ∈                                          {                                                      S                    1                                    ⁢                                                                          ⁢                  …                  ⁢                                                                          ⁢                                      S                    K                                                  }                            ⁢                                                          ⁢              K                                =                      Q            M                                              (        4        )            
Here Q is the number of signal points of the modulated data; for QPSK, Q=4, for 16QAM, Q=16, and for 64QAM Q=64. Thus in MLD the amount of computation for multivalued modulation becomes enormous, and moreover the amount of computation increases exponentially with the number of transmission antennas.
In MIMO-OFDM transmission and reception, the transmission portion of the transmitting station TR and reception portion of the receiving station RV in FIG. 21 are configured as an OFDM (Orthogonal Frequency Division Multiplexing) transmission portion and OFDM reception portion.
FIG. 22 shows the configuration of a MIMO-OFDM system of the prior art. On the transmitting side, the FEC encoder 1 uses a convolution code to perform encoding processing for error detection and correction on the input data stream, the puncture portion 2 performs puncture processing according to the coding rate for the encoded data series, and the spatial interleaver 3 divides the punctured data bit series into plural data streams, viz. Ns data streams in the figure. Then, the frequency interleavers 41 to 4Ns perform frequency interleaving in which the positions of the input serial data (subcarrier signal components) are interchanged. The constellation mappers 51 to 5Ns perform constellation mapping of the signal components for the number of subcarriers, according to the data modulation method (BPSK, QPSK, 16QAM, 64QAM, and similar) indicated by an adaptive control portion (not shown). Then, the precoder 6 multiplies each of the Ns group parallel data streams by a precoding matrix, and converts the Ns data streams into Nt data streams. Precoding processing is performed to obtain the advantages of transmission diversity and to reduce interchannel interference.
The IFFT (Inverse Fast Fourier Transform) portions 71 to 7Nt convert the series-input data from the precoder 6 into parallel data for the number of subcarriers, and then perform IFFT (inverse Fourier transform) of this parallel data, combining the data in discrete time signals (OFDM signals), which are output. The guard interval insertion portions 81 to 8Nt insert guard intervals into the OFDM signals input from the IFFT portions, and the transmission portions (TX) 91 to 9Nt perform DA conversion of the OFDM signals with guard intervals inserted, convert the OFDM signal frequency from the baseband frequency to the wireless band, performs high-frequency amplification, and transmits the signals from the antennas 101 to 10Nt.
Signals transmitted from the transmission antennas 101 to 10Nt pass through fading channels (propagation paths) and are received by reception antennas 111 to 11Nr of the reception device; reception circuits (Rx) 121 to 12Nr convert the RF signals received by the antennas into baseband signals, perform AD conversion to convert the baseband signals into digital signals, and output the signals. The symbol extraction portions 131 to 13Nr delete guard intervals GI and extract OFDM symbols with FFT timing, and input the results to the FFT portions 141 to 14r. The FFT portions 141 to 14r perform FFT processing for each extracted OFDM symbol, converting the symbols to frequency-domain subcarrier samples.
The channel estimation circuit 15 performs channel estimation for each subcarrier, and inputs channel state information CSI (channel H(k)) to the transmission side and MIMO processing portion 16. H(k) is channel matrix of the kth subcarrier.
The transmission-side precoder 6 determines the precoding matrix based on the notified channel, and an adaptive control portion, not shown, determines the data modulation method using an adaptive control, and inputs the results to each of the constellation mappers 51 to 5Ns.
For each subcarrier, the MIMO processing portion 16 uses the estimated channel to separate and output Ns transmission data streams from received signals according to a prescribed signal algorithm.
Subsequently, the constellation demappers 171 to 17Ns, frequency deinterleavers 181 to 18Ns, spatial deinterleaver 19, depuncture portion 20, and decoding portion 21 perform demapping processing, frequency deinterleave processing, spatial demapping processing, decoding processing to demodulate and decode the transmitted data, in the opposite order of the processes performed on the transmitting side, and outputs the results.
The transmission-side precoder 6 performs precoding by multiplying the Ns data streams by the precoding matrix F(k) which is in conformity with the spatial multiplexing precoding method, eigen-mode transfer precoding method, or limited feedback precoding method.
If the channel H(k) is unknown, the precoder 6 uses the identity matrix as the precoding matrix F(k), multiplying the transmission data stream by this precoding matrix F(k) to execute precoding (spatial multiplexing precoding method). If the channel state information CSI (channel H(k)) is known on the transmitting side and on the receiving side, then the precoder 6 performs singular value decomposition (SVD) of the channel matrix H(k), takes the decomposed matrix to be the precoding matrix F(k), and multiplies the transmission data stream by this matrix F(k) to execute precoding (eigen-mode transfer precoding method). And, if the precoding matrix F(k) adapted to the channel is fed back from the receiving side, then the precoder 6 multiplies the transmission data stream by this precoding matrix F(k) to execute precoding (limited feedback precoding method).
In the eigen-mode transfer precoding method and the limited feedback precoding method, the transmitting side must utilize channel state information (CSI) or partial CSI. Further, in the eigen-mode transfer precoding method, the transmitting side is required to perform adaptive modulation control and power control.
FIG. 23 shows another configuration of a conventional MIMO-OFDM communication device; differences with the MIMO-OFDM communication device of FIG. 22 are the provision of a STBC (Space Time Block Coding) portion 6′ in place of the precoder 6, and the STBC portion 6′ performs the space time block coding of each of Ns data streams and maps the results of the STBC to Nt antennas, in order to obtain transmission diversity. That is, the STBC encoders 61′ to 6Ns′ of the STBC portion 6′ perform STBC coding processing of data input from the respective constellation mappers 51 to 5Ns and output the results. FIG. 24 explains STBC encoding processing; the STBC encoder 6i′ converts continuous two-symbol data [x0,x1] with period T into two symbol data series. The first data series is [x0, −x1*], and the second data series is [x1,x0*]. Here “*” denotes the complex conjugate. The two data series are each OFDM processed and input to two transmission antennas. In FIG. 24, only the OFDM transmission configuration for one data stream is shown, but a similar configuration for the other data stream is comprised.
In the conventional MIMO-OFDM system (spatial multiplexing precoding method, eigen-mode transfer precoding method, limited feedback precoding method) of FIG. 22, in order to enhance performance, an MLD algorithm is used. And in the conventional hybrid STBC spatial multiplexing OFDM system of FIG. 23 or hybrid SFBC spatial multiplexing OFDM system also, in order to enhance performance, an MLD algorithm is used. Here “SFBC” is an abbreviation of “Space Frequency Block Coding”.
However, in the MLD algorithm an extremely large number of computations are performed, and so there is the problem that implementation using this algorithm entails enormous complexity. Hence in order to reduce complexity, a ZF algorithm or an MMSE algorithm is used, and in order to improve performance a repeated decoding algorithm (VBLAST algorithm) or similar is used. However, if a ZF algorithm or MMSE algorithm is used to reduce complexity, the problem of degradation of error rate performance occurs. And if the VBLAST algorithm is used to improve performance, there is the problem that the complexity and processing delay are increased. In conventional MIMO-OFDM communication, adaptive modulation control and power control based on the reception state must be adopted; this control further increases system complexity, and is undesirable from the standpoints of cost and power consumption. For the above reasons, a MIMO-OFDM communication method with improved performance, and which reduces system complexity, has been sought.
Among MIMO-OFDM systems of the prior art, there are systems which accurately predict channel parameters (see Japanese Patent Laid-open No. 2002-44051). Also, in other MIMO-OFDM systems of the prior art, there are decoders which combine ZF and MLD to simplify computation processing (see Japanese Patent Laid-open No. Tokuhyo 2006-509396). And, in still other MIMO-OFDM systems of the prior art, there are some which simplify computation processing when using VBLAST (see Japanese Patent Laid-open No. 2003-244103). However, these systems of the prior art are not systems which use a ZF algorithm or MMSE algorithm to reduce the complexity of MIMO decoding while improving performance.